@article{MAKHILLAJIT201615176366,
    title = {Advancement in Analysis of Preprocessing and Frequent
Patterns in Web Usage Mining},
    journal = {Asian Journal of Information Technology},
    volume = {15},
    number = {17},
    pages = {3407-3413},
    year = {2016},
    issn = {1682-3915},
    doi = {ajit.2016.3407.3413},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2016.3407.3413},
    author = {K.N.,K. and},
    keywords = {frequent patterns,conviction value,aggregative clustering,NBU similarity,Web log file preprocessing},
    abstract = {Enormous amount of information’s are gathered and viewed through world wide web by different
users. The user practices their views by entering hypertext credentials by internet with a large repository of web
pages and web usage mining process is essential for efficient web site management, personalization, business
and support services and network traffic flow analysis, etc., web page contains images, text, videos and other
multimedia and web log file holds the information of the user accesses in the websites. The log file shall have
some noisy and ambiguous data which may affect the data mining process and large quantity of web traffic
should be handled effectively to acquire desired information. So the log file should be preprocessed to improve
the quality of data. Preprocessing consists of data cleaning and data filtering, user identification and session
identification. Two sets of log files are collected and processed to obtain experimental results. This study
presents a framework for user and session preprocessing and clustering with Hidden Damage Data algorithm
(HDD) and also analyzes the navigational behavior of users through an enhanced Conviction Frequent Pattern
Mining Algorithm (CFPMA) to identify frequent patterns in web log data. The experimental result shows that
the proposed technique achieves low execution time and higher accuracy when compared with the other
existing methods.}
    }